A STATE-OF-THE-ART REVIEW OF NEURAL NETWORKS FOR PERMEABILITY PREDICTION

Abstract

This paper reviews the state-of-the-art of neural networks for permeability prediction from well logs. Good prediction of permeability is necessary for reservoir characterisation and is important for improving the reliability of the asset value of oil and gas companies. Two particular models, known as backpropagation and radial basis function networks, have been applied. From previous work, six innovative aspects are identified:

choice of inputs;

outlier detection and removal;

data splitting;

scaling;

multiple networks; and

prediction confidence.

We have also provided a list of future research directions in the area, reflecting the current deficiencies of the use of neural networks. The topics are:

the quality and quantity of core data;

the maximum use of the logs;

the compatibility of scales;

the use of soft computing; and

the management of prediction confidence.

The current applications are certainly the beginning of a new era. It is important for petrophysicists to take advantage of the advanced technologies.